Near-infrared spectroscopy used to predict soybean seed germination and vigour

Maythem Al-Amery; Robert L. Geneve; Mauricio F. Sanches; Paul R. Armstrong; Elizabeth B. Maghirang; Chad Lee; Roberval D. Vieira; David F. Hildebrand

doi:10.1017/S0960258518000119

Near-infrared spectroscopy used to predict soybean seed germination and vigour

Maythem Al-Amery, Robert L. Geneve, Mauricio F. Sanches, Paul R. Armstrong, Elizabeth B. Maghirang, Chad Lee, Roberval D. Vieira, David F. Hildebrand

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

Abstract

Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950-1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.

Original language	English
Pages (from-to)	245-252
Number of pages	8
Journal	Seed Science Research
Volume	28
Issue number	3
DOIs	https://doi.org/10.1017/S0960258518000119
State	Published - Sep 1 2018

Bibliographical note

Publisher Copyright:
© Cambridge University Press 2018 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited..

Funding

This is publication No. 17-11-117 of the Kentucky Agricultural Experiment Station and is published with the approval of the Director. Mention of company or trade names is for description only and does not imply endorsement by the USDA. The USDA is an equal opportunity provider and employer. This project was supported by the USDA National Institute of Food and Agriculture, hatch project numbers KY011042 and KY006062.

Funders	Funder number
U.S. Department of Agriculture
National Institute of Food and Agriculture	KY011042, KY006062
Kentucky Agricultural Experiment Station

Keywords

Glycine max
NIR spectroscopy
accelerated ageing
electrolyte leakage
vigour

ASJC Scopus subject areas

Plant Science

Access to Document

10.1017/S0960258518000119

Cite this

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title = "Near-infrared spectroscopy used to predict soybean seed germination and vigour",

abstract = "Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950-1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.",

keywords = "Glycine max, NIR spectroscopy, accelerated ageing, electrolyte leakage, vigour",

author = "Maythem Al-Amery and Geneve, {Robert L.} and Sanches, {Mauricio F.} and Armstrong, {Paul R.} and Maghirang, {Elizabeth B.} and Chad Lee and Vieira, {Roberval D.} and Hildebrand, {David F.}",

note = "Publisher Copyright: {\textcopyright} Cambridge University Press 2018 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited..",

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AU - Al-Amery, Maythem

AU - Geneve, Robert L.

AU - Sanches, Mauricio F.

AU - Armstrong, Paul R.

AU - Maghirang, Elizabeth B.

AU - Lee, Chad

AU - Vieira, Roberval D.

AU - Hildebrand, David F.

N1 - Publisher Copyright: © Cambridge University Press 2018 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited..

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N2 - Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950-1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.

AB - Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950-1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.

KW - Glycine max

KW - NIR spectroscopy

KW - accelerated ageing

KW - electrolyte leakage

KW - vigour

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Near-infrared spectroscopy used to predict soybean seed germination and vigour

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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